Regional Differences and Dynamic Evolution of Carbon Emission Intensity of Agriculture Production in China

Big data development and agricultural carbon emissions: Exacerbation or suppression? A quasi-natural experiment based on the establishment of the National Big Data Comprehensive Pilot Zone

As a strategic resource, big data has become a key force affecting carbon emission reduction in agriculture. However, its impacts remain controversial, and relevant empirical evidence remains to be explored. Based on quasi-natural experimental analysis, this study explored the impact and mechanism of the construction of the National Big Data Comprehensive Pilot Zone (NBDCPZ) on agricultural carbon emissions (ACE) in China and adopted a difference-in-difference (DID) model using China's provincial panel data from 2003 to 2020. The results showed that the ACE in the NBDCPZ establishment area was significantly reduced by 11.91%, a finding that remained robust following the parallel trend test and the placebo test, among others. Mechanism analysis showed that the ACE was reduced through industrial upgrading and technological innovation. Heterogeneity analysis showed that more pronounced policy gains were achieved in China's central-eastern regions as well as in non-major grain-producing areas compared to western and major grain-producing areas. This research provided supporting evidence for the prospect of big data application in ACE and provided useful guidance regarding the promotion of green and sustainable agricultural development.

Can digital financial inclusion converge the regional agricultural carbon emissions intensity gap?

To explore whether digital finance can reduce agricultural carbon emissions, promote regional convergence, and foster inclusivity in rural revitalization and shared prosperity, this paper uses the provincial-level index of digital financial inclusion to analyze the impact of digital financial inclusion on the intensity of agricultural carbon emissions and the Degum Gini coefficient (D-Gini coefficient) of regional carbon emission intensity in 30 sample provinces from 2010 to 2020. It examines the mechanism of the impact of digital financial inclusion on both variables to understand the underlying factors better. The main conclusions are as follows: (1) Digital financial inclusion significantly reduces the intensity of agricultural carbon emissions and narrows the gap in carbon emission intensity between regions. (2) The unconditional quantile regression coefficients show that the negative coefficients of the digital financial inclusion index and the three-dimensional indices decrease with increasing quantiles. However, the significant effects vary significantly at different quantiles. (3) Technological progress and the government's ability to allocate financial resources play a significant mediating role, and the income gap between urban and rural areas can be further narrowed, as well as the carbon emission intensity gap between provinces. The empirical results are robust and proven by replacing the econometric analysis method, changing the core variables, and other methods.

Spatial autocorrelation and driving factors of carbon emission density of crop production in China

Mitigating carbon emissions from crop production is essential for addressing global warming. At a macro-level, existing studies have often relied on the calculation of carbon emission intensity of crop production to understand comparable carbon effects between regions. However, this approach obscures the differences in crop planting scale and natural attributes across regions, leaving room for improvement in the methods and scope of analysis. To extend the existing research, we proposed an idea for calculating the carbon emission density of crop production based on planting area. Additionally, we developed an analytical framework for driving factors of carbon emission density of crop production from a spatial interaction perspective. The provincial carbon emission density of crop production in mainland China between 2000 and 2020 was calculated, and spatial econometric models were utilized to investigate the spatial autocorrelation and driving factors. The results indicate that the national average carbon emission density of crop production was 1.462 t/hm2 annually. Over 21 years, the carbon emission density of agricultural materials, rice cultivation, soil management, and straw burning evolved from 0.384 to 0.470 t/hm2, 0.409 to 0.367 t/hm2, 0.171 to 0.169 t/hm2, and 0.317 to 0.448 t/hm2, respectively. The global Moran's index indicated a positive spatial autocorrelation of carbon emission density of crop production and the subdivided carbon sources among provinces. Regarding direct effects, an increase in the proportion of paddy fields in cropland composition and irrigation efficiency would significantly promote the carbon emission density, while factors such as cropland area, multiple cropping, agricultural personnel numbers, departmental proportion, and disaster degree would decrease the local carbon emission density. Certain factors, such as cropland area and agricultural disasters, had a spatial spillover effect on carbon emission density between provinces. The study suggests harnessing key drivers and spatial spillover effects to achieve regional low-carbon crop production.

Effect of agricultural fiscal expenditures on agricultural carbon intensity in China

Few studies provide direct evidences that agricultural fiscal affects agricultural carbon intensity. This study tries to fill this gap. Using panel data of 30 provinces in China from 2005 to 2019, we conclude that agricultural fiscal expenditures significantly reduce agricultural carbon intensity. The result is still robust after employing the provincial agricultural leaders' birthplace information as an instrumental variable. Further study shows that the negative effect of agricultural fiscal expenditures on agricultural carbon intensity is more pronounced in regions with less corruption and is also more visible in central, western, and inland regions than other areas. For this effect, agricultural technological improvement and structure optimization are possible channels, but not operation scale expansion. Interestingly, although agricultural fiscal expenditures reduce the local agricultural carbon intensity, neighbor regions' carbon intensities are increased due to fiscal rivalry.

Spatial-temporal evolution of carbon emissions and spatial-temporal heterogeneity of influencing factors in the Bohai Rim Region, China

The total change in carbon emissions in the Bohai Rim Region (BRR) plays a guiding role in the policy formulation of carbon emission reduction in northern China. Taking the 43 cities in the BRR as an example, the spatial-temporal evolution of carbon emissions in the BRR was analyzed using kernel density estimation (KDE), map visualization, and standard deviation ellipses, and the spatial autocorrelation model was used to explore the spatial clustering of carbon emissions. On this basis, the spatial-temporal heterogeneity of the factors influencing carbon emissions is explained using a Geodetector. The results are as follows: (i) During the study period, the carbon emissions in the BRR were on the rise, the share of carbon emissions in the Beijing-Tianjin-Hebei region (BTHR) and Liaoning Province was decreasing, and the contribution of Shandong Province was gradually enhanced. The spatial distribution of carbon emissions shows a geographical pattern of "middle-high and low-outside." (ii) Carbon emissions from different regions show the characteristics of BTHR > Shandong Province > Liaoning Province. The high-value carbon emission area continues to move from the northwest of Beijing-Tianjin-Hebei to the southeast. (iii) Municipal carbon emissions showed a significant positive spatial correlation in the later part of the study. The high-high aggregation area is in Tianjin, and the low-low aggregation area is in Liaoning Province. (iv) The level of transport development contributes to carbon emissions with the highest growth rate, followed by industrial structure. There are also regional differences in the dominant influences on municipal carbon emission differences. Population size, urbanization, and economic development level are the core influencing factors of carbon emissions in the BTHR, Shandong Province, and Liaoning Province, respectively. In addition, the explanatory power of the interaction between the level of economic development and other factors on carbon emissions is at a high level.

Examination of the relationship between agricultural carbon emission efficiency and food quality and safety: from the perspective of environmental regulation

An important breakthrough in the coordinated development of China's low-carbon goals and food security strategies is agricultural development oriented toward quality, safety, green, and low carbon. This study integrated command-control and market-incentive environmental regulation (ER), agricultural eco-efficiency (ACEE), and food quality and safety (FQS) into a unified theoretical framework. The unexpected output-oriented Super-SBM model was used to calculate the ACEE of China's provinces and cities from 2011 to 2020 and test the bidirectional causality between ACEE and FQS through the system generalized moment estimation model. A dynamic panel smooth transition (PSTR) model was used to explore the nonlinear impact mechanisms of different types of ERs on ACEE and FQS. The results showed that there was a long-term, two-way causal relationship between ACEE and FQS. The impact of environmental regulations on ACEE and FQS has a nonlinear relationship. Among them, the role of market-incentivized ER is more significant. Therefore, building an interregional coordinated development mechanism, improving the utilization rate of agricultural resources such as fertilizers and pesticides, and coordinating the positive effects of different types of ERs are the keys to improving the ACEE and ensuring the coordinated development of FQS.

Spatial-temporal pattern and spatial convergence of carbon emission intensity of rural energy consumption in China

Based on the panel data of 30 provinces (municipalities and autonomous regions) in China from 2005 to 2019, this paper uses Gini coefficient decomposition and kernel density estimation to investigate the regional differences and dynamic evolution trend of rural energy carbon emission intensity in China. Then, the convergence model is used to analyze the convergence characteristics and influencing factors of carbon emission intensity. The study found the following: (1) During the observation period, the carbon emissions of coal energy and oil energy were much higher than those of gas energy. The carbon emissions of rural energy consumption experienced three stages of development, and the carbon emission intensity showed a downward trend as a whole. The spatial distribution pattern of total carbon emissions present an "adder" distribution, and the spatial agglomeration phenomenon gradually strengthens with the passage of time. (2) The Gini coefficient of China's rural energy consumption carbon emission intensity shows a trend of "Inverted N-shaped." The Gini coefficient of carbon emission intensity in the eastern and northeastern regions shows an increasing trend, while the Gini coefficient of carbon emission intensity in the western and central regions shows a downward trend. The super variable density is the main source of carbon emission intensity difference. The peak value of the main peak of the nuclear density curve of the carbon emission intensity increased significantly, the bimodal form evolved into a single peak form, and the density center moved to the left. (3) The carbon emission intensity of rural energy consumption in the whole, central, and western regions of China has the characteristic of σ convergence, while the carbon emission intensity in the eastern and northeastern regions does not have the characteristic of σ convergence. There is a significant spatial positive correlation in the carbon emission intensity, there is also a significant β convergence characteristic, the speed of conditional β convergence is significantly higher than that of absolute β convergence, and the spatial interaction will further improve the convergence speed. Industrial structure, industrial agglomeration, and energy efficiency will increase the convergence speed. In terms of sub-regions, the conditional convergence rate of carbon emission intensity in the four regions shows a decreasing trend in the northeast, central, eastern, and western regions.

Spatiotemporal evolution of carbon emission intensity and the driving effect of green technology innovation: Evidence from China

The "double carbon" goal has proposed new "green" requirements for China's low-carbon economic development, and green technology innovation (GTI) has become an important way to coordinate economic and sustainable development. The study explores the spatial-temporal evolution of carbon emission intensity (CEI) of Chinese prefecture-level cities, analyses the nonlinear impact of GTI on the CEI by constructing a panel quantile model, and draws the following conclusions. First, CEI shows a decreasing trend from 2006 to 2019 and a spatial distribution pattern of "high in the north and low in the south, high in the west and low in the east". Second, GTI significantly reduces CEI, and as the quantile point increases, the carbon reduction effect of GTI is characterized by a U-shaped change, decreasing first and then increasing. Overall, GTI has a significantly more profound inhibiting effect on high CEI regions than on low CEI regions. Third, there is spatial heterogeneity in the impact of GTI on CEI across the four major regions and diverse policy contexts. The study proposes countermeasures for low-carbon development in terms of tapping the potential of GTI, strengthening its regional synergy, and applying locally appropriate measures, to gain the great practical significance for achieving the double carbon target.

Reducing the carbon emission from agricultural production in China: do land transfer and urbanization matter?

Urbanization and land transfer have triggered a profound reform of the Chinese agricultural sector since reform and opening, leading to a continuous rise in agricultural carbon emissions. However, the impact of urbanization and land transfer on agricultural carbon emissions is not widely understood. Therefore, based on the panel data covering 30 provinces (cities) in China from 2005 to 2019, we adopted a panel autoregressive distributed lag model and a vector autoregressive model to empirically explore the causal relationship between land transfer, urbanization, and agricultural carbon emissions. The main conclusions are as follows: (1) Land transfer can significantly reduce carbon emissions from agricultural production in the long run, while urbanization has a positive effect on agricultural carbon emissions. (2) In the short run, land transfer has a significant positive impact on agricultural carbon emissions, and urbanization also has a positive impact on the carbon emissions of agricultural production, but in insignificant ways. (3) There is two-way causality between land transfer and agricultural carbon emission, and between urbanization and land transfer is the same, but urbanization is the one-way Granger cause of agricultural carbon emissions. Finally, some suggestions are provided for low-carbon agriculture development: the government should encourage the transfer of land management rights and guide high-quality resources to gather in green agriculture.

Spatiotemporal characteristics and determinants of agricultural carbon offset rate in China based on the geographic detector

This paper attempts to explore the spatiotemporal variation characteristics of the agricultural carbon offset rate (ACOR) and the reasons that shape its differentiation characteristics in China. To achieve this objective, the Dagum Gini coefficient, kernel density estimation, and geographic detector model are employed in this study. The results show that there are some differences in ACOR among regions in China. Interregional differences are the main source of their overall variation. Excluding the spatial conditions, the ACOR of each province in the sample period shows low mobility characteristics. Considering the spatial conditions, there is convergence in the lower-middle neighborhoods. The three-year lag period did not significantly affect the interaction of ACOR between regions under the accession time horizon. At the aggregate level, the spatial and temporal divergence in China's ACOR is driven by urbanization rate, agricultural fiscal expenditure, and rural education level. As for the regional level, the scale of household farmland operation plays a greater role in determining the spatiotemporal variation of the eastern and central regions' ACOR. While urbanization rate is more determinant for the western region, the interaction between any two factors has significantly higher explanatory power for the spatial and temporal variation of ACOR than the single factor.

The influences of the advancement of green technology on agricultural CO2 release reduction: A case of Chinese agricultural industry

The development of green technology (GT) may have a vital influence in decreasing carbon releases, and the linkage between the advancement of GT and CO2 releases in China's agricultural industry has not attracted enough attention. The main objectives of this study are to assess the influence of agricultural green technology advancement on efficiency enhancement, release control capabilities, agricultural energy structure, and agriculture industrial structure. This article decomposes the advancement of green technology (AGTP) in the agricultural industry in China into resource-saving green technology advancement (AEGTP) and emission reduction green technology advancement (ACGTP). At the same time, to evaluate the intermediary impact of green technology advancement, a two-step econometric model and an intermediary impact model were utilized to evaluate the panel data of 30 provinces in China from 1998 to 2018. The role of AGTP (including ACGTP and AEGTP) and CO2 release concentration has also been explored critically. The results show that (i) under the two-step measurement method, AGTP has substantial favorable impacts on agricultural energy efficiency (EF) and possesses a negative impact on agriculture industrial structure (PS) and agricultural energy structure (ES). Agricultural energy efficiency (EF) and agriculture industrial structure (PS) under AGTP will reduce CO2 release concentration, but the path of agricultural energy structure (ES) will increase CO2 release concentration. (ii) At the national level, AGTP has an immediate unfavorable influence on CO2 releases. After introducing the intermediary variables, the intermediary impact of AGTP on CO2 releases through agricultural energy efficiency (EF), agriculture industrial structure (PS), and agricultural energy structure (ES) is also significantly negative, and the direct impacts of each variable are higher than the intermediary impact. (iii) In terms of different zones, the direct impacts of AGTP are all significant. The order of significance of the direct impacts of different zones is west to central and central to eastern. The overall significance ranking of the mediating impact is ACGTP > AEGTP > AGTP, and the significance ranking of each index is ES > EF > PS. Finally, this article puts forward some policy recommendations to reduce CO2 releases.

Organic carbon sequestration in Chinese croplands under compost application and its contribution to carbon neutrality

Achieving the carbon neutrality in China has great impact on alleviating global warming. Compost application, an important measure to promote soil organic carbon (SOC) sequestration, has been practiced in China since 2015. However, it is still unclear how much carbon can be fixed by cropland soil under compost application in the whole China. China has pledged to strive for the goal of carbon neutrality by 2060, which brought two issues: whether compost application can consistently promote SOC sequestration until 2060, and how much contribution it can make to the carbon neutrality. In the present study, we analyzed the results from 93 literatures to determine the SOC sequestration under compost application in the different agricultural divisions of China. Results showed that there were regional differences in the effect of compost application on SOC sequestration. The annual SOC sequestration in Northern China (NC) and Gansu + Xinjiang (GX) was significantly high than other regions. In addition, the annual SOC sequestration was negatively related to the duration of the experiment, while the accumulative SOC sequestration during the experimental period increased with the increase of the duration. According to our results, the total SOC sequestration in topsoil of Chinese cropland was 85 Tg C year^-1 under compost application, which will make a 4.4% contribution to carbon neutrality during 2021-2060. In conclusion, cropland soil in China can still sequester carbon for more than 35 years under compost application. Thus, abidingly promoting compost application in China is crucial to accomplishing the carbon neutrality goal.

The effect of China's leading officials' accountability audit of natural resources policy on provincial agricultural carbon intensities: the mediating role of technological progress

China is one of the largest agricultural countries in the world. In the context of China's efforts to achieve dual carbon goals (carbon peak and carbon neutral), the need for carbon emissions reductions in the agricultural sector cannot be ignored. This study collected 2007 to 2018 data from 30 Chinese provinces and used a difference in differences (DID) model to investigate the relationships between China's leading officials' accountability audit of natural resources policy (LOAANR), agricultural technological progress, and agricultural carbon emissions intensities (CEI). A common trend test, placebo test, PSM-DID, and other test methods were used to verify the reliability of the results. The results show that (1) compared with the non-pilot areas, the policy implementation could significantly reduce CEI; (2) the LOAANR was able to stimulate patented technological progress (ATI) and mechanical technological progress (AMT), but different types of technological progress had different mediation effect sizes; and (3) the policy effects shows obvious regional heterogeneity, manifesting as west > east > central; and the policy effects were more obvious in the non-major grain-producing areas, but had no inhibition effects on the CEI in the major grain-producing areas; compared with low intensity market-based environmental regulation (MER) regions, high-intensity MER regions have stronger LOAANR emission reduction effects. Based on the study findings, policy suggestions are given to reduce agricultural carbon emissions and promote higher quality agricultural development.

Analysis of Dynamic Evolution and Spatial-Temporal Heterogeneity of Carbon Emissions at County Level along "The Belt and Road"-A Case Study of Northwest China

Northwest region is the main energy supply and consumption area in China. Scientifically estimating carbon emissions (CE) at the county level and analyzing the spatial-temporal characteristics and influencing factors of CE in a long time series are of great significance for formulating targeted CE reduction plans. In this paper, Landscan data are used to assist NPP-VIIRS-like data to simulate the CE from 2001 to 2019. Spatial-temporal heterogeneity of CE was analyzed by using a two-stage nested Theil index and geographically and temporally weighted regression model (GTWR). The CE in northwest China at the county increases yearly while the growth rate slows down from 2001 to 2019. The spatial pattern forms a circle expansion centered on the high-value areas represented by the provincial capital, which is also obvious at the border between Shaanxi and Ningxia. Axial expansion along the Hexi Corridor is conspicuous. The spatial pattern of CE conforms to the Pareto principle; the spatial correlation of CE in northwest counties is increasing year by year, and the high-high agglomeration areas are expanding continuously. It is an obvious high carbon spillover effect. Restricted by the ecological environment, the southwest of Qinghai and the Qinling-Daba Mountain area are stable low-low agglomeration areas. The spatial pattern of CE in northwest China shows remarkable spatial heterogeneity. The difference within regions is greater than that between regions. The "convergence within groups and divergence between groups" changing trend is obvious. According to the five-year socioeconomic indicators, the economic scale (GDP), population scale (POP), and urbanization level (UR) are the main influencing factors. The direction and intensity of the effect have changed in time and space. The same factor shows different action intensities in different regions.

Spatiotemporal variations and structural characteristics of carbon emissions at the county scale: a case study of Wu'an City

In China, the county is not only an important component of industrial areas and a large contributor of carbon emissions, but also a key administrative unit for the implementation of carbon peak and carbon neutrality goals and policies. The spatiotemporal variations and structural characteristics of carbon emissions at the county scale are of great significance to China's dual goals of regional carbon policy implementation and low carbon spatial planning. Thus, it is important and insightful to conduct an in-depth and detailed examination of these characteristics while focusing on a typical iron and steel industry county-level city in North China. This study systematically calculated the carbon emissions of the county-level city of Wu'an from 2008 to 2017, and explored their structural characteristics and spatiotemporal variations. The results showed that (1) under the influence of macroeconomic and national policies, the carbon emissions of county-level cities dominated by the iron and steel industry show obvious phased characteristics; (2) there is a significant negative correlation between industry carbon emission concentrations and industrial carbon emissions; (3) within the steel industry system, sintering, iron smelting, steelmaking, and metal product processing are the main sources of carbon emissions, and the coal-based production process of the iron and steel industry needs a fundamental reformation; and (4) the carbon emission of Wu'an City shows obvious spatial differentiation characteristics. The geographic distribution of carbon emissions in Wu'an City is very unbalanced and tended to cluster together in urban areas, industrial and mining areas, and major towns. Taking 2014 as the turning point, the spatial pattern of carbon emissions in Wu'an City presents different variation characteristics.

Impacts of Land-Use Change on the Spatio-Temporal Patterns of Terrestrial Ecosystem Carbon Storage in the Gansu Province, Northwest China

Land-use change is supposed to exert significant effects on the spatio-temporal patterns of ecosystem carbon storage in arid regions, while the relative size of land-use change effect under future environmental change conditions is still less quantified. In this study, we combined a land-use change dataset with a satellite-based high-resolution biomass and soil organic carbon dataset to determine the role of land-use change in affecting ecosystem carbon storage from 1980 to 2050 in the Gansu province of China, using the MCE-CA-Markov and InVEST models. In addition, to quantify the relative size of the land-use change effect in comparison with other environmental drivers, we also considered the effects of climate change, CO2 enrichment, and cropland and forest managements in the models. The results show that the ecosystem carbon storage in the Gansu province increased by 208.9 ± 99.85 Tg C from 1980 to 2020, 12.87% of which was caused by land-use change, and the rest was caused by climate change, CO2 enrichment, and ecosystem managements. The land-use change-induced carbon sequestration was mainly associated with the land-use category conversion from farmland to grassland as well as from saline land and desert to farmland, driven by the grain-for-green projects in the Loess Plateau and oasis cultivation in the Hexi Corridor. Furthermore, it was projected that ecosystem carbon storage in the Gansu province from 2020 to 2050 will change from −14.69 ± 12.28 Tg C to 57.83 ± 53.42 Tg C (from 105.62 ± 51.83 Tg C to 177.03 ± 94.1 Tg C) for the natural development (ecological protection) scenario. By contrast, the land-use change was supposed to individually increase the carbon storage by 56.46 ± 9.82 (165.84 ± 40.06 Tg C) under the natural development (ecological protection) scenario, respectively. Our results highlight the importance of ecological protection and restoration in enhancing ecosystem carbon storage for arid regions, especially under future climate change conditions.

Carbon Emission Intensity Characteristics and Spatial Spillover Effects in Counties in Northeast China: Based on a Spatial Econometric Model

Under the “double carbon” target, it is important to reduce carbon emissions in each region. Using exploratory spatial data analysis (ESDA), the center of gravity method, and spatial econometric models, we analyzed the characteristics and spatial spillover effects of carbon emission intensity in counties in Northeast China from 2000 to 2020 and made recommendations to the government for more reasonable carbon reduction strategies in order to achieve sustainable development. The results were as follows: (1) Since 2000, the carbon emission intensity in Northeast China has increased after first declining, and the carbon emission intensity in the western and northern regions of Northeast China has increased faster than Northeast China’s average. (2) After 2000, the spatial aggregation of carbon emission intensity has improved in Northeast China. (3) Northeast China’s carbon emission intensity has a positive spatial spillover effect. Through the feedback mechanism, the growth in population size, the rise in economic development level, the level of industrialization as well as the rise in living standard, the land use structure dominated by arable land and construction land, and the increase in urbanization level in the region will cause the carbon emission intensity in the surrounding areas to increase. An increase in public expenditures leads to a decrease in carbon emission intensity in the adjacent area. (4) When the vegetation cover exceeds its threshold value, it can have a larger inhibitory influence on carbon emission intensity. To summarize, each county in Northeast China is a carbon emission reduction community, and policymakers must consider the spatial spillover effect of carbon emission intensity when developing policies.

Evaluation of City–Industry Integration Development and Regional Differences under the New Urbanization: A Case Study of Sichuan

As an important focus of China’s new urbanization construction, the policy of “city–industry integration” has played a key role in promoting the sustainable development of urban construction. This paper integrates the three major elements of social service, ecology and environment, and science and technology into the traditional indicator system of “industry, city, and people”. Firstly, the PSO-AHP-EM model was constructed to empower the indicators subjectively and objectively, and the degree of city–industry integration was sorted by the GRA-TOPSIS model. Secondly, the Gini coefficient and Theil index were used to further explore the non-equilibrium of city–industry integration development. Finally, an empirical study was conducted on 18 prefecture-level cities in Sichuan Province from 2010 to 2019, and their development laws were analyzed. The results show that: (1) The weights obtained by the PSO-AHP solution were better than the weights obtained by AHP. (2) The level of city–industry integration in Sichuan was not high, and most cities need to be further developed. (3) The regional difference of city–industry integration was relatively small, and the main source of the difference was Chengdu Plain Economic Zone. This paper puts forward some suggestions to provide scientific support for the evaluation of city–industry integration.

Carbon Mitigation Pathways of Urban Transportation under Cold Climatic Conditions

Climate heterogeneity has enormous impacts on CO₂ emissions of the transportation sector, especially in cold regions where the demand for in-car heating and anti-skid measures leads to high energy consumption, and the penetration rate of electric vehicles is low. It entails to propose targeted emission reduction measures in cold regions for peaking CO₂ emissions as soon as possible. This paper constructs an integrated long-range energy alternatives planning system (LEAP) model that incorporates multi-transportation modes and multi-energy types to predict the CO₂ emission trend of the urban transportation sector in a typical cold province of China. Five scenarios are set based on distinct level emission control for simulating the future trends during 2017–2050. The results indicate that the peak value is 704.7–742.1 thousand metric tons (TMT), and the peak time is 2023–2035. Energy-saving–low-carbon scenario (ELS) is the optimal scenario with the peak value of 716.6 TMT in 2028. Energy intensity plays a dominant role in increasing CO₂ emissions of the urban transportation sector. Under ELS, CO₂ emissions can be reduced by 68.66%, 6.56% and 1.38% through decreasing energy intensity, increasing the proportion of public transportation and reducing the proportion of fossil fuels, respectively. Simultaneously, this study provides practical reference for other cold regions to formulate CO₂ reduction roadmaps.

Spatio-Temporal Disparities of Mariculture Area Production Efficiency Considering Undesirable Output: A Case Study of China’s East Coast

Mariculture areas are an important non-renewable natural resource and continuously improving their efficiency is important for increasing mariculture output and adjusting its structure. The aim of this study was to measure the mariculture area production efficiency (MAPE) considering undesirable outputs, further analyze its spatiotemporal disparities, and analyze the reasons for the differences observed during the period from 2008 to 2019. The super-efficiency Engel–Blackwell–Miniard (S-EBM) model and global Malmquist–Luenberger (GML) index was selected to analyze the technical efficiency and productivity of MAPE from both the static and dynamic aspects, and the Theil index was used to decompose the regional differences. The results showed that the MAPE showed fluctuation and an increasing trend overall; the production efficiency and technical progress showed a fluctuating rising trend, and technical progress had a significant driving effect on the production efficiency; and intra-regional differences were the main factors that cause the differences in MAPE. The findings suggest the increase of scientific and technological investment in mariculture, changes in mariculture methods, the establishment of environmental monitoring centers in mariculture areas, and the sharing of information technology between regions to achieve sustainable development.

Research on the Relationship between Prices of Agricultural Production Factors, Food Consumption Prices, and Agricultural Carbon Emissions: Evidence from China’s Provincial Panel Data

China is a large agricultural country with a high level of agricultural carbon emissions. Whether market prices can be used in agricultural production as a means of agricultural carbon emissions reduction is of great significance to improve the allocation of agricultural production factors and expand large-scale production. This paper applies an autoregressive distributed lag–pooled mean group(ARDL–PMG) model to evaluate the relationship between agricultural production factor prices, food consumption prices, and agricultural carbon emissions, using Chinese provincial panel data from 1994 to 2018. The results show that agricultural carbon emissions and agricultural production factor prices show environmental Kuznets curve (EKC) characteristics; agricultural carbon emissions and food prices show a U-shaped curve; and agricultural production factors are positively correlated with food price in both directions in the long-term. The results of Granger causality tests show that price is the cause of agricultural carbon emissions; the price of agricultural production factors and the price of food consumption are mutually causal. Such results have implications for price, agriculture, and environmental policies. The analysis implies that the market price can be applied to agricultural carbon reduction, which will help policymakers to implement effective price policies in order to reduce agricultural carbon emissions. One implication is that promoting the marketization of agricultural production factors and reducing price distortions will be conducive to carbon emissions reduction in agriculture, which in turn will increase food consumption prices. Therefore, subsidies are needed at the consumption end, which will eventually achieve further carbon emissions reduction at the production and consumption ends.